Tissue-based absolute quantification using large-scale TMT and LFQ experiments

Harnessing fine fibers in decellularized adipose-derived matrix for enhanced adipose regeneration

Decellularized Adipose-Derived Matrix (DAM) has the function of inducing adipogenesis, but the distribution of adipogenesis is uneven. We found for the first time that DAM contains two structural components: The tough fibers DAM (T-DAM) and the fine fibers DAM (F-DAM). T-DAM was a dense vortex structure composed of a large number of coarse fibers, characterized by myoblast-related proteins, which cannot achieve fat regeneration and forms a typical "adipose-free zone". While the F-DAM was a loose structure consisting of uniform fine fibers, has more matrix-related proteins and adipose-related proteins. It can not only better promote the adhesion and proliferation of adipose stem cells in vitro, but also achieve the regeneration of adipose tissue in vivo earlier and better, with a uniform range of adipogenesis. The F-DAM is the main and effective kind of DAM to initiate adipose tissue regeneration, which can be picked out by ultrasound fragmentation.

Expression Atlas update: insights from sequencing data at both bulk and single cell level

Expression Atlas (www.ebi.ac.uk/gxa) and its newest counterpart the Single Cell Expression Atlas (www.ebi.ac.uk/gxa/sc) are EMBL-EBI's knowledgebases for gene and protein expression and localisation in bulk and at single cell level. These resources aim to allow users to investigate their expression in normal tissue (baseline) or in response to perturbations such as disease or changes to genotype (differential) across multiple species. Users are invited to search for genes or metadata terms across species or biological conditions in a standardised consistent interface. Alongside these data, new features in Single Cell Expression Atlas allow users to query metadata through our new cell type wheel search. At the experiment level data can be explored through two types of dimensionality reduction plots, t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP), overlaid with either clustering or metadata information to assist users' understanding. Data are also visualised as marker gene heatmaps identifying genes that help confer cluster identity. For some data, additional visualisations are available as interactive cell level anatomograms and cell type gene expression heatmaps.